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Record W1534160520 · doi:10.1159/000106041

Upper Abdominal Malignancies: Intensity-Modulated Radiation Therapy

2007· review· en· W1534160520 on OpenAlexaff
Mojgan Taremi, Jolie Ringash, Laura A. Dawson

Bibliographic record

VenueFrontiers of radiation therapy and oncology · 2007
Typereview
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineRadiation therapyRadiologyRadiation treatment planningClinical trialNuclear medicineInternal medicine

Abstract

fetched live from OpenAlex

Local control and survival of most upper abdominal malignancies are poor. Challenges associated with the safe delivery of tumoricidal doses of radiation therapy to these malignancies include organ motion due to breathing, gastrointestinal filling and peristalsis, and the presence of many normal tissues with a low tolerance to radiation. Intensity-modulated radiation therapy (IMRT) can facilitate normal tissue sparing and dose escalation to these tumors, which has the potential to reduce toxicity and improve local control. Planning studies have demonstrated the potential for dose escalation with IMRT. However, degradation of upper abdominal IMRT plans in the presence of organ motion has also been demonstrated. Thus, organ motion reduction and image guidance strategies should be implemented in conjunction with IMRT. Clinical experience with dose-escalated IMRT is limited, and IMRT should continue to be studied in clinical trials before it is routinely used for upper abdominal malignancies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.335
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2007
Admission routes1
Has abstractyes

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